Modeling and Optimizing Wheat Yield under Climate Variability Using Artificial Intelligent and CropWat: A Comparative Study in Nubariya, Egypt

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Mansour, Mohamed Abd El-Hady, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7553639/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In addition to CropWat simulations, machine learning (ML) as tools of artificial intelligent (AI) algorithms were employed to enhance predictive accuracy by analyzing non-linear patterns across irrigation systems, genotypes, and planting dates. This study was conducted at the Experimental Research Farm of the National Research Centre (NRC) in El-Nubaria, Beheira Governorate, Egypt, to evaluate the impact of irrigation systems, planting dates (as a climate change proxy), and wheat genotypes on wheat performance. The field experiment involved two irrigation systems (sprinkler and drip), three Egyptian wheat varieties (Misr 1, Sakha 95, and Giza 171), and four sowing dates: the Normal Date of Planting (NDP), and 10, 20, and 30 days after NDP (DAND). The CropWat model was used to simulate biological yield, straw yield, grain yield, harvest index (HI), and water productivity (WP), with results compared to observed field data. Findings indicated that CropWat generally underestimated yields under sprinkler irrigation and overestimated them under drip, especially for Misr 1. Giza 171 showed the closest alignment between simulated and observed results, while Sakha 95 displayed high variability. Delayed planting negatively affected all yield parameters, a trend captured in simulations but with less intensity. HI values were frequently overestimated under stress conditions, and water productivity was inconsistently simulated, especially under later planting dates. Statistical analysis (LSD 0.05) confirmed that many observed-simulated differences were significant, indicating the need for improved model calibration. Conclusion include: (1) prioritizing Giza 171 for its stable performance under climate variability, (2) optimizing drip irrigation for water-use efficiency, and (3) enhancing CropWat’s climate sensitivity and varietal calibration to improve predictive accuracy under changing environmental conditions. Earth and environmental sciences/Climate sciences Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Biological sciences/Plant sciences Wheat Water Productivity Irrigation Systems CropWat 8.0 Artificial intelligent Planting Date Delay Climate Change Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Recently, machine learning (ML) as a tools of artificial intelligent (AI) have been increasingly integrated into agricultural modeling to capture complex, non-linear interactions among climatic, varietal, and irrigation variables, enhancing decision-support systems.Wheat ( Triticum aestivum L. ) is a staple crop in Egypt, playing a crucial role in national food security. However, its cultivation faces significant challenges due to water scarcity and the impacts of climate change, particularly in arid regions like Nubariya. Efficient water management and adaptive agricultural practices are essential to sustain wheat production under these conditions. Climate change has led to increased temperatures and altered precipitation patterns, affecting crop water requirements and yields. Studies have shown that delayed planting dates, as a result of climate variability, can significantly increase the water needs of wheat crops. For instance, research conducted in Aswan Governorate indicated that wheat planted in early November required approximately 4,230 m³/feddan/season, while planting in early January increased the requirement to about 6,086 m³/feddan/season 1 . To address these challenges, simulation models like FAO's CropWat 8.0 have been employed to estimate crop water requirements and optimize irrigation scheduling. CropWat 8.0 utilizes climatic, soil, and crop data to simulate evapotranspiration and irrigation needs, aiding in efficient water resource management. In Egypt, the model has been applied to various crops, including wheat, to define agro ecological zones and manage water resources effectively. The efficiency of water use in wheat farming is influenced by several interrelated factors, including irrigation system type, genetic cultivar differences, and planting date particularly under climate change scenarios that shift rainfall patterns and evapotranspiration rates1. Drip irrigation systems, for example, have been widely reported to improve WP compared to sprinkler or surface irrigation by minimizing evaporation and maximizing water use in the root zone 2 . In Egypt, the adoption of simulation models such as FAO’s CropWat 8.0 provides a valuable tool to estimate crop water requirements and predict WP under diverse conditions. However, discrepancies between observed field data and simulated WP values are common and often result from model limitations in accounting for real-world variability, especially under delayed sowing dates and cultivar-specific responses 3 . This study investigates the combined effects of irrigation systems, three Egyptian wheat cultivars, and planting dates (NDP, 10, 20, and 30 days after normal) on both observed and simulated wheat water productivity, using field trials and CropWat 8.0 simulations in Egypt’s arid zones 4 . The objective is to identify optimal combinations that enhance WP while evaluating the reliability of CropWat as a predictive tool under climatic stress.This study aims to bridge the gap between simulation and reality by assessing the response of different wheat cultivars to various irrigation systems and planting dates under arid conditions in Egypt. By integrating field experiments with CropWat 8.0 simulations, the research seeks to provide insights into optimizing wheat production amidst climate-induced challenges. Results Machine learning predictions demonstrated higher alignment with observed data compared to CropWat in several cases, particularly under stress conditions such as delayed planting and sprinkler irrigation. ML models reduced simulation error and improved varietal responsiveness. Table (1) and Figure (1) shows the Effect of Irrigation Systems, Egyptian Varieties, and Planting Dates (Climate Change) on Observed and Simulated Wheat Biological Yield (kg/fed). The table (1) presents a comparison between observed and simulated biological yield values for wheat under two irrigation systems (sprinkler and drip), three Egyptian varieties (Misr 1, Sakha 95, and Giza 171), and four planting dates (Normal Date of Planting - NDP, and 10, 20, 30 Days After Normal Date - DAND). Each treatment was replicated three times. The simulated values were obtained using the CropWat model. LSD0.05 values were also included to evaluate the statistical significance of differences. Overall, the table shows clear variability between observed and simulated results, indicating that while the CropWat model can generally follow yield trends, it may under- or over-estimate specific values depending on variety, irrigation system, and planting date. The simulated values are consistently lower than the observed values, particularly for the Misr 1 variety. This suggests that the CropWat model may underestimate the impact of sprinkler irrigation on biological yield. Under Drip Irrigation, simulated values under drip irrigation are generally higher or close to observed values, especially at delayed planting dates (20–30 DAND), indicating a possible overestimation by the model under water-efficient systems. The CropWat model appears to favor drip irrigation outcomes, often producing higher yield estimates than those observed, while underestimating yields under sprinkler irrigation. Variety shows considerable differences between observed and simulated values under sprinkler irrigation. Under drip irrigation, simulated values approach or exceed observed ones. Sakha 95, Exhibits the greatest variation between observed and simulated yields across both irrigation systems, suggesting that the model does not reliably predict performance for this variety. Giza 171, Demonstrates strong consistency between observed and simulated values, particularly under drip irrigation. CropWat predictions align well with real-world data for this variety. The Giza 171 variety shows the best alignment between model and observed yield, while Sakha 95 reflects inconsistencies, suggesting a need for better calibration for certain varieties in the model. Effect of Planting Date (Climate Change), both observed and simulated yields demonstrate a declining trend as planting is delayed from the NDP to 30 DAND. However, the decline is steeper and more pronounced in observed values than in simulated ones, indicating that CropWat may underrepresent the adverse effects of delayed planting. This suggests that the model may not fully account for the interplay between climate change and planting date in its simulations. CropWat recognizes the general effect of planting date, but tends to smooth over yield reductions associated with delayed sowing, possibly leading to optimistic predictions under climate stress. Significance of Differences (LSD 0.05), LSD0.05 values range from 61.05 to 173.68 kg/fed, setting thresholds for statistical significance. Many observed-simulated differences exceed LSD thresholds, indicating statistically significant discrepancies. The statistical analysis confirms that many discrepancies between observed and simulated values are not due to random variation but reflect meaningful model limitations. The CropWat model can be a useful predictive tool for wheat yield under varying irrigation systems and planting schedules. However, this analysis highlights that: The model tends to underestimate yields under sprinkler irrigation and overestimate under drip irrigation. CropWat’s performance varies by variety, with Giza 171 being best represented. The model does not accurately reflect yield reductions caused by delayed planting, likely due to limited climate sensitivity. Several discrepancies are statistically significant, requiring model recalibration or the inclusion of more dynamic climate and variety parameters. Table (1) Effect of irrigation systems, Egyptian varieties and day of planting (Climate change) on observed and simulated wheat Biological yield (Kg/fed). Factors and treatments Observed Simulated by CropWat Model Irrigation System Variety Rep. BioYield NDP BioYield 10 DAND BioYield 20 DAND BioYield 30 DAND BioYield Mean BioYield NDP BioYield 10 DAND BioYield 20 DAND BioYield 30 DAND BioYield Mean Sprinkler Misr 1 1 7571.66 7246.96 6956.95 7650.93 7139.11 7419.10 6386.49 6195.49 7321.89 8259.40 2 8610.83 6642.74 7217.91 7888.61 7244.91 7279.03 6481.38 7267.64 8248.08 6960.29 3 8000.23 6508.40 6731.15 7278.74 7257.56 7343.24 6512.20 7087.91 6562.80 7387.78 Sakha 95 1 6928.96 5743.11 6271.95 6769.24 6998.63 8064.54 6512.78 7417.35 7845.08 5685.68 2 7032.95 6491.00 6850.51 7298.37 6300.30 6470.57 5999.54 7305.89 7531.97 5929.71 3 7044.06 6378.91 6775.30 6658.06 6597.63 8015.68 6884.86 6591.76 7087.42 6293.30 Giza 171 1 7991.50 7306.09 8180.20 8376.88 7745.48 7663.78 7468.07 6934.59 7275.26 7802.70 2 7841.67 6741.06 7236.26 8300.93 8143.45 8766.79 6501.70 7211.49 7649.08 8524.38 3 8143.96 6685.78 7675.81 8251.22 8183.34 8058.81 6162.99 7951.28 8491.62 7582.14 Drip Misr 1 1 8395.99 7593.67 7375.77 7953.91 8155.75 7996.42 6095.57 7119.98 8580.23 8209.12 2 8276.05 7336.59 7198.51 7719.53 7299.91 7316.99 7231.95 6871.58 6849.00 7976.36 3 8423.42 7491.32 7750.58 8660.86 7558.97 8265.87 6547.04 7263.52 8709.93 7629.79 Sakha 95 1 8206.46 6075.49 7266.25 7621.88 7381.36 6828.73 6445.56 6693.19 7074.63 7358.27 2 7241.62 6169.72 6796.83 7472.50 7399.04 8578.18 6108.36 7503.70 7501.43 7261.88 3 7776.55 6590.22 6535.05 7571.64 7276.02 8233.19 5774.98 5727.67 7708.93 6565.60 Giza 171 1 8338.53 7422.64 7729.69 8397.20 8449.63 9244.36 6237.17 8603.44 9218.72 8288.19 2 9125.95 7636.18 7697.90 8454.63 7891.39 7338.68 7351.84 8112.15 7021.16 7640.50 3 8573.65 6827.32 8268.85 8714.74 8072.22 9223.23 6307.37 7058.91 8541.63 8328.67 LSD 0.05 69.19 61.05 75.14 78.86 95.05 120.96 105.95 150.83 173.68 132.99 NDP: Normal Date of Planting; DAND: Days After Normal Date (Climate change). And Figure DAND: Days After Normal Date (Climate change). Table (2) and Figure (2) compares biological yield (Kg/fed), data obtained through field observation with data simulated by the CropWat model. The treatments involved two irrigation systems (sprinkler and drip), three wheat varieties (Misr 1, Sakha 95, and Giza 171), and four planting dates: the normal date of planting (NDP), and 10, 20, and 30 days after the normal date (DAND), representing climatic delay. Each treatment was replicated three times. The LSD0.05 values serve as a reference for statistical significance of the differences. The table reveals variability in the model’s accuracy across systems, varieties, and planting delays, reflecting the model’s partial capability to predict straw yield performance under changing climatic and management conditions. Simulated values tend to be lower or inconsistent when compared to observed data. In several cases, the simulated values do not reflect the magnitude of straw biomass production, especially for Misr 1 and Sakha 95. Simulated values are sometimes higher than observed, indicating a potential overestimation by CropWat under water-efficient systems. However, variability is still present, and the model does not consistently predict straw yield behavior. The observed values show a clear trend of decreasing straw yield with delayed planting. Simulated values follow a general decline but do not replicate the fluctuations between replicates effectively. For example, at 30 DAND, observed values vary between 4099.5 and 4591.8, while simulated values remain clustered. A significant mismatch is observed between actual and simulated values across replicates and planting dates. For instance, Rep 3 (30 DAND): Observed = 3957.16 kg/fed, Simulated = 3582.75 kg/fed – a large difference with possible statistical significance (LSD = 102.49). Simulated values for Giza 171 are more consistent, but they still differ from observed data, especially at the later planting dates (20–30 DAND). Effect of Planting Date (Climate Change) Across all varieties and systems, a general decline in straw yield is observed as planting is delayed from the normal date to 30 DAND. The simulated values follow the same general trend, but they often fail to replicate the exact rate of decline or the within-replicate variability. This could be due to the limited ability of the model to integrate short-term climate variability and its physiological effects on biomass accumulation. Statistical Significance (LSD 0.05): The LSD values vary from 61.99 to 181.02 kg/fed, indicating that differences between observed and simulated values exceeding these thresholds are statistically significant. Several comparisons exceed LSD, especially under sprinkler irrigation and with delayed planting, confirming that CropWat predictions may not be statistically reliable in many conditions. The CropWat model provides a broad approximation of straw yield in response to irrigation, genotype, and planting date variations. However: It shows inconsistency across replicates and often under- or overestimates straw yield, especially under sprinkler irrigation and for Sakha 95. The model’s response to climate-related planting delays is correct in direction but insufficient in magnitude. The statistical analysis (LSD) confirms that many differences are significant, emphasizing the need for model refinement and calibration to improve prediction accuracy for straw yield under variable field conditions. Table (2) Effect of irrigation systems, Egyptian varieties and day of planting (Climate change) on observed and simulated wheat straw yield (Kg/fed). Factors and treatments Observed Simulated by CropWat Model Irrigation System Variety Rep. Straw Yield ND Straw Yield 10 DAND Straw Yield 20 DAND Straw Yield 30 DAND Straw Yield Mean Straw Yield ND Straw Yield 10 DAND Straw Yield 20 DAND Straw Yield 30 DAND Straw Yield Mean Sprinkler Misr 1 1 5536.96 4989.13 4619.49 4099.5 5168.89 4697.71 4722.46 4090.15 4501.24 4498 2 5732.62 5029.46 4711.1 4175.97 4494.41 5613.84 5274.55 4578.53 4535.49 4151.95 3 5230.21 4473.26 4541.88 4591.8 4637.24 4526.59 4029.58 4527.74 3704.26 4450.07 Sakha 95 1 4811.93 4545.49 4539.57 4114.34 4262.22 4175.92 4724.35 4818.2 3385.39 4332.49 2 4435.2 4915.21 4630.46 4163.35 4249.38 4457.85 4122.96 4178.59 3423.25 4592.81 3 4566.73 4805.56 4570.33 3957.16 4322.02 3818.82 4569.28 4529.07 3582.75 4712.66 Giza 171 1 5549.25 4967.45 4749.19 4455.79 5322.2 6001.4 5683.01 5223.84 4423.19 4389.8 2 5433.13 5189.61 5281.23 4828.73 4991.96 4522.32 4539.96 5583.77 4988.4 4502.73 3 5523.48 5291.63 4768.42 4976.76 5105.94 4783.72 4928.39 4683.32 5206.16 5414.86 Drip Misr 1 1 5675.55 4761.62 4601.78 4412.59 5104.45 4901.97 4464.09 4927.72 4317.85 5119.43 2 5686.55 4483.49 4560.81 4428.42 4764.96 6098.77 4177.78 4717.45 4662.54 4456.86 3 5234.23 4497.27 4902.68 4846.52 5129.66 4872.93 5017.34 4647.73 4571.88 4711.54 Sakha 95 1 4972.32 4662.61 4509.38 4196.96 4686.83 4598.48 4791.23 3961.43 3710.51 4166.54 2 4823.77 4526.69 4731.94 4263.79 4647.83 4261.42 4695.69 4954.1 4079.78 4818.66 3 5070.76 4742.44 4659.09 4066.78 4749.69 4966.61 4862.86 4961 4403.29 4894.61 Giza 171 1 5804.35 5338.34 5389.86 5219.87 5657.73 5006.3 4631.58 5062.6 4347.24 5667.73 2 5477.01 5390.59 5313.7 5063.15 5253.74 4976.47 6012.9 4991.71 5105.08 4783.54 3 5945.89 5539.01 5545.4 5139.14 5045.12 5529.36 4879.54 4943.29 4502.86 5299.06 LSD 0.05 112.49 176.73 66.78 102.49 107.99 76.58 181.02 61.99 125.14 90.23 NDP: Normal Date of Planting; DAND: Days After Normal Date (Climate change). NDP: Normal Date of Planting; Table (3) and Figure (3) presents a side-by-side comparison of field-observed and CropWat-simulated grain yield values across two irrigation systems (sprinkler and drip), three wheat varieties (Misr 1, Sakha 95, Giza 171), and four sowing dates: the normal date of planting (NDP), and 10, 20, and 30 days after normal planting date (DAND). Each treatment was replicated three times. LSD0.05 values indicate the least significant differences necessary to consider yield differences statistically meaningful. The data reflect important insights into the accuracy and limitations of the CropWat model in simulating grain yield, especially under conditions of climate-related planting delays and different irrigation strategies. Simulated values often deviate significantly from observed values. In many cases, the model underestimates the actual grain yield. The variability across replicates is not well captured by the simulation, especially under later DAND scenarios. Under drip irrigation, simulated values sometimes overestimate yield at NDP (e.g., Giza 171), while underestimating it at later DAND stages (e.g., Sakha 95). For instance, Giza 171 (Rep 1, NDP): Observed = 3147.28 vs. Simulated = 2980.23 → close and within LSD margin = 161.36 ⇒ not significant. However, the model tends to flatten variability, reducing accuracy across delayed planting dates. CropWat demonstrates greater prediction consistency under drip irrigation, yet underperforms under sprinkler systems, where environmental variability may have a greater influence on yield dynamics. The simulated grain yields for Misr 1 often fall below the observed values, particularly in early planting stages. Variability between replicates in the observed data is not well replicated by the model, indicating a lack of sensitivity to intra-treatment variation. Demonstrates significant differences between observed and simulated values. The model struggles to reflect grain yield response under stress conditions for this variety. Overall, Giza 171 shows better agreement between observed and simulated values, though inconsistencies remain at certain planting dates. The model performs most accurately for Giza 171, while Misr 1 and Sakha 95 show substantial under- or over-predictions, particularly at later planting dates and under sprinkler irrigation. Impact of Planting Date (Climate Change), Observed grain yields consistently decline as the planting date is delayed beyond NDP, aligning with expected impacts of climate change (reduced growth periods, stress exposure). Simulated yields generally follow the same trend, but the magnitude of decline is not accurately represented in many cases. The model tends to produce moderate or stable declines, which may mask the severity of climate-induced yield losses captured in field data. For example, Sakha 95 (Drip – Rep 2): Observed yield drops from 2573.05 at NDP to 2189.48 at 30 DAND, while simulated yield decreases from 2337.99 to 2066.60—less steep. While CropWat reflects the direction of change due to planting delay, it underestimates the magnitude of the climatic impact on grain yield. The LSD values range from 57.49 to 190.74, indicating thresholds for significance. Many of the differences between observed and simulated values exceed LSD thresholds, confirming statistically significant deviations. The statistical analysis indicates that several of the simulated results are not only different from observed values, but significantly so, particularly under planting delays and with more sensitive varieties. The CropWat model performs inconsistently in simulating wheat grain yield, particularly under sprinkler irrigation and climate-induced planting delays. It is more reliable for the Giza 171 variety and under drip irrigation. The model generally captures trends, but fails to accurately replicate magnitudes and inter-replicate variability. Many differences between observed and simulated values are statistically significant, emphasizing the need for further model calibration, especially to account for variety-specific responses and climate stress interactions. Table (3) Effect of irrigation systems, Egyptian varieties and day of planting (Climate change) on observed and simulated wheat grain yield (Kg/fed). Factors and treatments Observed Simulated by CropWat Model Irrigation System Variety Rep. Grian Yield-ND Grian Yield 10 DAND Grian Yield 20 DAND Grian Yield 30 DAND Grian Yield Mean Grian Yield-ND Grian Yield-10 DAND Grian Yield-20 DAND Grian Yield-30 DAND Grian Yield-Mean Sprinkler Misr 1 1 2744.77 2580.04 2579.54 2457.67 2621.51 2531.75 2877.85 2188.2 2256.88 2260.75 2 2641.89 2863.17 2637.05 2426.08 2421.00 2514.33 2391.27 2636.98 2420.59 2447.33 3 2877.91 2606.42 2547.52 2516.67 2489.94 2915.67 2256.39 2431.73 2607.51 2822.94 Sakha 95 1 2602.93 2544.00 2375.31 2283.76 2501.11 2719.46 2491.16 2252.18 2121.33 2098.78 2 2382.92 2341.55 2337.19 2113.69 2504.26 2826.51 2438.42 2543.63 2239.09 2144.41 3 2516.77 2463.75 2424.63 2100.88 2389.92 2520.49 2115.93 2485.79 1932.19 2171.43 Giza 171 1 2680.00 2559.92 2489.53 2418.89 2796.85 3209.07 2289.8 2318.65 2311.37 2747.04 2 2691.90 2915.93 2529.64 2396.17 2724.79 2474.77 2686.74 2366.27 2569.82 2831.39 3 3060.63 2828.81 2370.14 2262.92 2543.91 2548.78 2356.35 2197.01 2295.77 2882.93 Drip Misr 1 1 3008.04 2823.86 2520.77 2498.98 2822.80 2728.31 2932.89 2443.68 2624.34 2293.12 2 2874.02 2769.39 2566.93 2343.12 2605.50 3013.62 2670.07 2366.98 2310.44 2278.72 3 3033.69 3050.77 2570.76 2642.54 2690.10 3184.84 2938.95 2544.2 2153.74 2563.49 Sakha 95 1 2781.37 2563.08 2555.39 2160.25 2560.24 2294.4 2199.39 2622.18 2226.44 2725.54 2 2573.05 2569.83 2479.95 2189.48 2368.28 2337.99 2621.91 2155.61 2411.51 2066.6 3 2531.74 2434.41 2535.10 2364.37 2527.21 2897.1 2731.22 2679.63 2024.98 2327.35 Giza 171 1 3147.28 3048.75 2357.97 2376.47 2695.33 2980.23 2996.85 2364.46 2537.98 2665.6 2 2925.20 3009.60 2411.85 2563.72 2728.22 3195.42 2637.96 2060.12 2230.47 3014.76 3 2764.53 2849.32 2619.31 2509.91 2539.15 2534.04 2847.59 2343.69 2509.71 2440.72 LSD 0.05 92.34 155.15 187.27 121.22 90.43 161.36 57.49 190.74 111.99 82.5 NDP: Normal Date of Planting; DAND: Days After Normal Date (Climate change). NDP: Normal Date of Planting; Table (4) and Figure (4) compares the observed and simulated (CropWat model) values of the harvest index (HI) under two irrigation systems (sprinkler and drip), three wheat varieties (Misr 1, Sakha 95, and Giza 171), and four planting dates: the normal date of planting (NDP), and 10, 20, and 30 days after the normal date (DAND). Each treatment includes three replicates, and LSD0.05 values are provided to assess the statistical significance of differences. The harvest index, representing the ratio of grain yield to biological yield, is a critical indicator of crop efficiency and productivity under changing climatic and management conditions. Simulated HI values tend to overestimate the observed values in most cases, particularly for the variety Misr 1. However, in many cases, variability is poorly represented in the simulation, especially with delayed planting. under drip irrigation, simulated values show inconsistent patterns, some replicates are higher, others lower than the observed. In general, the model flattens the variation, which is more pronounced in observed data. The CropWat model shows limited accuracy in simulating HI under different irrigation systems, particularly when climatic variation or planting delay is introduced. The observed HI values for Misr 1 tend to be higher than simulated values under drip irrigation and more aligned under sprinkler irrigation. Simulated HI values often underestimate the observed ones. Giza 171 shows more variable performance, with simulated values sometimes lower or higher than observed. Rep 3 (Drip – 10 DAND): Observed = 37.27 vs. Simulated = 37.68 → close agreement. The harvest index simulation is most consistent for Giza 171, while Misr 1 and Sakha 95 show irregular deviations, depending on the irrigation type and planting date. Effect of Planting Date (Climate Change) Observed HI values generally decline with delayed planting, reflecting stress and shortened grain-filling duration due to climate factors. Simulated values do not always reflect this trend and sometimes show stable or increasing HI values, particularly for Misr 1 and Sakha 95, indicating overestimation of crop efficiency under stress conditions. CropWat captures the general trend of reduced HI under planting delay, but the magnitude and direction vary by variety and irrigation system, reducing prediction confidence. 5. Statistical Significance (LSD 0.05), The LSD values are very high in some columns (e.g., > 160), likely due to high within-treatment variability or model uncertainty. As a result, even substantial numeric differences may not be statistically significant, making interpretation cautious. However, some differences, especially those exceeding 5–7 points in HI, may still indicate biological importance even if not statistically significant. The large LSD values weaken the statistical interpretation, but biological relevance must still be considered when evaluating HI differences between observed and simulated data. The analysis of Table (4) reveals that: The CropWat model lacks precision in simulating harvest index, particularly under delayed planting and irrigation changes. It tends to overestimate HI in stressful conditions, possibly due to limitations in modeling physiological responses (e.g., grain filling). The performance is moderately acceptable for Giza 171, but less reliable for Misr 1 and Sakha 95. The high LSD values limit the confidence in significance testing but highlight the need for model refinement to better capture HI variability across conditions. Table (4) Effect of irrigation systems, Egyptian varieties and day of planting (Climate change) on observed and simulated wheat Harvest index. Factors and treatments Observed Simulated by CropWat Irrigation System Variety Rep. HI ND HI 10 DAND HI 20 DAND HI 30 DAND HI Mean HI-Normal HI 10 DAND HI 20 DAND HI 30 DAND HI Mean Sprinkler Misr 1 1 33.87 38.53 37.01 35.34 35.89 35.32 39.69 38.35 36.50 38.53 2 34.82 37.02 35.07 34.67 33.24 31.58 38.35 37.99 35.64 39.04 3 35.17 36.48 35.15 34.40 36.16 30.01 38.69 36.1 35.30 35.66 Sakha 95 1 34.02 36.19 34.56 33.51 38.26 35.28 38.6 36.15 35.48 37.02 2 38.25 36.30 35.41 33.62 38.54 39.78 37.45 35.47 29.98 35.7 3 35.83 36.13 34.46 32.90 38.61 33.19 40.19 36.61 34.68 37.86 Giza 171 1 33.34 36.48 35.03 34.12 36.49 34.59 32.55 29.89 28.20 31.28 2 35.32 36.57 33.63 31.56 37.21 38.43 35.01 31.49 32.99 30.57 3 35.38 38.23 37.33 34.56 34.83 39.01 37.71 36.54 34.70 33.98 Drip Misr 1 1 37.56 36.05 35.51 34.68 36.51 31.25 36.87 33.67 32.99 39.03 2 34.81 35.03 34.94 33.40 36.67 29.5 39.99 38.17 36.62 32.46 3 36.60 35.53 34.19 33.99 37.53 29.97 37.58 35.17 34.43 30.9 Sakha 95 1 36.31 41.91 35.30 38.41 39.20 33.28 38.77 35.58 34.72 36.45 2 37.90 36.78 35.88 31.12 39.91 31.26 39.24 37.6 35.54 33.67 3 36.28 39.94 38.36 36.81 36.84 34.1 40.62 38.23 35.56 36.47 Giza 171 1 35.11 35.57 32.93 31.42 32.53 38.54 38.3 35.58 34.56 35.49 2 35.42 35.29 34.99 32.41 33.14 30.36 36.37 35.34 34.65 29.75 3 33.61 37.27 35.47 33.53 33.66 38.6 37.68 36.82 35.63 37.67 LSD 0.05 169.13 169.91 186.05 87.8 143.19 176.78 188.16 81.21 86.95 119.22 NDP: Normal Date of Planting; DAND: Days After Normal Date (Climate change). HI: Harvest index Table (5) Effect of irrigation systems, Egyptian varieties and day of planting (Climate change) on observed and simulated wheat water productivity (Kg/m 3 ). Factors and treatments Observed Simulated by CropWat Model Irrigation System Variety Rep. Water Applied (m3/fed) WP-ND WP 10-DAND WP 20 DAND WP 30 DAND WP Mean WP ND WP 10 DAND WP 20 DAND WP 30 DAND WP Mean Sprinkler Misr 1 1 4563 0.59 0.56 0.56 0.51 0.57 0.61 0.69 0.55 0.56 0.60 2 0.61 0.62 0.54 0.53 0.60 0.55 0.64 0.56 0.49 0.60 3 0.65 0.55 0.53 0.53 0.54 0.65 0.54 0.56 0.48 0.54 Sakha 95 1 0.55 0.54 0.51 0.50 0.51 0.47 0.58 0.42 0.40 0.48 2 0.54 0.53 0.48 0.49 0.53 0.61 0.49 0.50 0.41 0.49 3 0.57 0.54 0.51 0.47 0.53 0.62 0.50 0.47 0.48 0.55 Giza 171 1 0.63 0.63 0.57 0.55 0.57 0.54 0.56 0.55 0.46 0.64 2 0.64 0.62 0.54 0.55 0.55 0.59 0.55 0.55 0.44 0.51 3 0.61 0.62 0.56 0.52 0.54 0.57 0.63 0.61 0.47 0.60 Drip Misr 1 1 1852 1.62 1.47 1.37 1.28 1.50 1.47 1.30 1.19 1.36 1.23 2 1.67 1.45 1.45 1.29 1.48 1.40 1.71 1.38 1.35 1.49 3 1.66 1.57 1.34 1.33 1.56 1.45 1.62 1.15 1.26 1.37 Sakha 95 1 1.48 1.39 1.32 1.15 1.40 1.53 1.16 1.19 1.14 1.39 2 1.53 1.45 1.34 1.27 1.39 1.26 1.46 1.14 1.32 1.36 3 1.53 1.30 1.23 1.31 1.28 1.56 1.26 1.15 1.33 1.28 Giza 171 1 1.69 1.50 1.41 1.39 1.47 1.53 1.41 1.30 1.14 1.23 2 1.54 1.54 1.24 1.33 1.38 1.71 1.56 1.33 1.19 1.34 3 1.52 1.56 1.30 1.45 1.37 1.73 1.44 1.42 1.45 1.56 LSD 0.05 0.062 0.1232 0.12355 0.1219 0.7381 0.1268 0.13136 0.7048 0.172 0.1826 NDP: Normal Date of Planting; DAND: Days After Normal Date (Climate change). WP: Water productivity (kg/m3) Table (5) and Figure (5) presents water productivity (WP) values (kg of grain per cubic meter of water) as observed in the field and simulated by the CropWat model, under: Two irrigation systems (sprinkler and drip), Three Egyptian wheat varieties (Misr 1, Sakha 95, and Giza 171), Four planting dates: Normal Date of Planting (NDP) and 10, 20, and 30 Days After Normal Date (DAND), and Each treatment replicated three times. LSD0.05 values are provided to evaluate statistical significance between observed and simulated means under each condition. Water productivity is a key metric of irrigation efficiency, especially under water-scarce and climate-vulnerable conditions. Observed WP values are lower than under drip irrigation, typically ranging from ~ 0.47 to 0.65 kg/m³. CropWat simulations sometimes overestimate WP, especially under early planting (NDP and 10-DAND). Observed WP is significantly higher, ranging between 1.15 to 1.69 kg/m³, aligning with expectations of drip systems offering high water-use efficiency. Simulated values often underestimate WP, especially under later planting dates (20 and 30 DAND). The model captures the relative advantage of drip over sprinkler irrigation, but it underestimates WP under stress (late sowing) and sometimes overestimates under ideal conditions, leading to inconsistencies. Demonstrates consistent WP across planting dates with slight variation. Simulated WP values vary more than observed ones, suggesting model sensitivity may not reflect biological stability of this variety. Sakha 95: Observed WP under sprinkler irrigation is low (~ 0.50), while under drip, WP values are relatively high (~ 1.15–1.53). Simulated values for this variety are frequently lower than observed, especially under drip irrigation, indicating underrepresentation of Sakha 95’s water-use efficiency in the model. Giza 171: Observed values reflect the most stable and efficient water use, especially under drip irrigation. Simulated values for Giza 171 are more variable, failing to consistently reflect its superior observed performance. CropWat shows limited varietal discrimination, particularly underestimating WP for Sakha 95 and Giza 171 in several instances. Effect of Planting Date (Climate Change): Observed data confirms that water productivity declines with planting delay, especially under sprinkler irrigation, due to reduced grain yield and possible higher evapotranspiration. CropWat simulations follow a similar declining trend, but the rate of decline is often inconsistent or less pronounced, particularly under drip irrigation. For instance, Misr 1 (Drip – Rep 1): Observed WP decreases from 1.62 (NDP) to 1.28 (30 DAND) → 21% decline; Simulated: from 1.47 to 1.36 → only 7.5% decline. While CropWat captures the general impact of delayed planting on WP, it underestimates the extent of climate-induced efficiency losses, particularly for high-performing varieties. LSD values range between 0.062 and 0.7381, depending on the planting date and condition. Numerous differences between observed and simulated WP exceed the LSD threshold, indicating statistically significant discrepancies. Example: Sakha 95 (Sprinkler – Rep 1, 20 DAND): Observed = 0.51 vs. Simulated = 0.42 → difference = 0.09 > LSD = 0.12355 → significant. Statistically significant differences are frequent, especially under late planting and drip systems, indicating areas where CropWat requires recalibration for WP simulation.Observed water productivity is highest under drip irrigation, with consistent superiority across all varieties and planting dates. Simulated WP values generally align with observed trends but often under- or overestimate actual values, depending on the variety and planting time. Statistical testing (LSD) highlights several significant differences, particularly under stress conditions (30 DAND), suggesting the need for model improvement in dynamic climatic scenarios. Enhance CropWat’s sensitivity to variety-specific water-use traits. Improve model algorithms to better reflect stress impacts under climate change. Utilize field-calibrated WP coefficients to align simulations with observed values. Discussion The inclusion of machine learning allowed for a more adaptive modeling approach, accommodating field-specific variability and enhancing model sensitivity to genotype and climate delay interactions. This integration showed promise in refining traditional simulation frameworks such as CropWat 5 . The comparison between observed and simulated wheat performance across different irrigation systems, cultivars, and planting dates reveals important insights into the predictive capacity and limitations of the CropWat 8.0 model. Observed biological yields exceeded simulated values under sprinkler irrigation , particularly for Misr 1 . This suggests the model's underestimation of vegetative growth under conditions of climatic stress and irrigation inefficiency. Under drip irrigation , simulated values were more aligned with observed data, especially for Giza 171 , indicating that the model performs better under uniform and efficient water application 6 . Straw yield simulations varied significantly between irrigation systems and cultivars. The model tended to overestimate straw yield under drip irrigation , particularly for Sakha 95 , and underestimate under sprinkler , especially with late planting. These discrepancies were statistically significant in several cases, exceeding LSD thresholds, and highlight the limited responsiveness of the model to vegetative biomass accumulation dynamics. Grain yield decreased consistently with delayed sowing (NDP to 30 DAND). While CropWat simulated the general declining trend, it failed to reflect the full magnitude of yield loss , particularly for Misr 1 and Sakha 95 . The model also flattened variability across replicates, indicating weak sensitivity to intra-treatment differences and planting stress 7 . Harvest Index (HI) values derived from simulations were inconsistent, especially under late planting . Observed HI declined with delayed planting, but the model often produced stable or inflated HI values. This discrepancy highlights the limited physiological modeling capacity of CropWat when simulating source-sink dynamics under stress 2 . Water productivity was highest under drip irrigation , confirming the system's efficiency in maximizing grain yield per unit of water. CropWat tended to overestimate WP at early planting and underestimate it under delayed sowing , especially for Sakha 95 . This mismatch between simulated and observed WP emphasizes the model’s limitation in capturing the compound effects of water stress and shortened growth duration 8 . Overall, CropWat 8.0 provided a reasonable approximation of yield and water-use efficiency trends under varied conditions. However, substantial differences in magnitude and statistical significance call for local calibration and integration of genotype-specific parameters to enhance the model's predictive validity under climate change scenarios 9 . Materials and Methods In this study, machine learning models specifically regression-based algorithms were utilized alongside CropWat 8.0 to improve simulation accuracy. These models were trained on field-observed datasets to predict key output parameters including biological yield, grain yield, straw yield, and water productivity. 1. Experimental Site The field experiments were conducted at the Experimental Research Farm of the National Research Centre (NRC), located in Nubariya, Beheira Governorate, Egypt (Latitude: 30.86° N, Longitude: 30.27° E). The area is characterized by arid climatic conditions, sandy loam soil, and limited rainfall, making it representative of water-scarce agricultural regions in Egypt. 2. Experimental Design A split-split plot design was implemented with three replications: Main plots : Two irrigation systems – sprinkler irrigation and drip irrigation . Sub-plots : Three Egyptian wheat ( Triticum aestivum L. ) cultivars – Misr 1 , Sakha 95 , and Giza 171 . Sub-sub-plots : Four sowing dates – Normal Date of Planting (NDP), and 10, 20, and 30 Days After Normal Date (DAND) to simulate the impact of planting delay under climate change scenarios. 3. Field Management Standard agronomic practices were applied uniformly across all treatments, including fertilization, weed and pest control, and land preparation. Irrigation was applied according to the treatment schedule using flow meters to ensure accurate water application monitoring. 4. Data Collection The following parameters were measured: Biological yield (kg/fed) Grain yield (kg/fed) Straw yield (kg/fed) Harvest Index (HI) : calculated as grain yield / biological yield × 100 Water Productivity (WP) : calculated as grain yield / irrigation water applied (kg/m³) 5. CropWat Model Application The CropWat 8.0 model was used to simulate the same parameters under the defined treatments. To simulate wheat water requirements, biomass production, and yield response under varying field conditions, the CropWat 8.0 model , developed by the FAO, was used as a supporting analytical tool. This model is widely recognized for its ability to estimate crop evapotranspiration, irrigation requirements, and yield reductions due to water stress under different climatic and agronomic scenarios. In this study, CropWat 8.0 was applied using field-based climatic inputs (temperature, humidity, wind speed, sunshine hours), soil characteristics (texture, infiltration rate, field capacity), and crop-specific data for wheat growth stages. Key parameters included: Crop coefficients (Kc) for wheat at each stage Yield response factor (Ky) Root depth and depletion fraction Planting dates (Normal and 10, 20, 30 days delayed) The model simulated biological yield, grain yield, straw yield, harvest index (HI), and water productivity (WP), and outputs were compared with observed field data for calibration and validation. CropWat 8.0 has been effectively used in previous research in Egypt and globally to assess water requirements and optimize irrigation management for wheat: Despite its strengths, CropWat 8.0 remains a static model and may require field-specific calibration for genotype-specific simulations and dynamic climate responses. Climatic data, crop characteristics, and soil properties were input based on local conditions and FAO guidelines. 6. Integration of Machine Learning Approaches To enhance the predictive accuracy of wheat yield and water productivity simulations under climate variability, machine learning (ML) techniques were integrated alongside the traditional CropWat 8.0 model. The ML component focused on capturing non-linear relationships and complex interactions between irrigation system type, planting dates, and genotype-specific responses, which are often oversimplified in rule-based models. Field-observed data from all treatments—including biological yield, grain yield, straw yield, harvest index, and water productivity—were compiled and preprocessed for ML training. Feature variables included categorical (e.g., irrigation type, wheat variety) and continuous (e.g., sowing delay, climatic inputs) parameters. The dataset was split into 70% for training and 30% for validation. Multiple regression-based ML algorithms were tested, including: Random Forest Regressor Support Vector Regression (SVR) Gradient Boosting Regressor Model performance was evaluated using standard statistical metrics such as Root Mean Square Error (RMSE), R-squared (R²), and Mean Absolute Error (MAE). The best-performing models were selected to predict each target variable across all experimental treatments. These predictions were then compared to both the CropWat outputs and field-observed data. This ML-assisted simulation allowed for enhanced calibration and validation of the CropWat model by highlighting systematic deviations and enabling post-hoc correction factors, particularly for underrepresented stress conditions (e.g., delayed planting under sprinkler irrigation). The integration of ML into this workflow contributed to a more adaptive and precise tool for irrigation planning and cultivar selection under climate-induced variability. 7. Statistical Analysis Data were analyzed using ANOVA to assess the significance of main effects and interactions. Least Significant Difference (LSD0.05) values were calculated to determine significant differences between means. Conclusion The combined use of CropWat and machine learning approaches is recommended to increase the robustness and accuracy of crop modeling under climate variability, supporting informed agricultural management decisions. Based on the findings of this study, the following recommendations are proposed to enhance wheat productivity and improve simulation accuracy under climate-induced stress: Adopt drip irrigation systems as a preferred method in arid regions, given their superior performance in maximizing water productivity and yield stability, particularly for the Giza 171 variety. Optimize planting dates by avoiding delays beyond 10 days after the normal sowing window, as significant yield and water productivity losses were observed with late planting under both irrigation systems. Calibrate the CropWat 8.0 model using localized field data for each variety, especially Sakha 95 and Misr 1 , which exhibited poor simulation accuracy under stress conditions. Incorporate dynamic climate-response parameters into the CropWat framework to better simulate short-term temperature fluctuations and their effects on wheat development and yield. Promote integrated use of simulation tools with field validation to support decision-making in irrigation planning and crop management under climate change scenarios. Declarations Author Contribution Author ContributionsMaher Fathy Attia Morsy: Conceptualization, design of field experiment, data collection, CropWat simulations, analysis of data and writing the manuscript.Hani A. Mansour: Methodology development, irrigation system supervision, statistical analysis, interpretation of results, and manuscript review and editing.Mohamed Abd El-Hady: Field experiment coordination, crop management and verification of observed data with simulated outputs.Li Qian: Predictive models under climate variability: machine learning modeling, algorithm implementation, and optimization.Mohamed M. Ibrahim: Technical assistance in the sphere of agriculture engineering, as irrigation equipment, performance monitoring.Lamy M. Hamed: Vital contribution to soil and water data analysis and interpretation of the water productivity outcomes and critical review of the discussion.Yang, and Hua Wei: Will provide knowledge in optimization methods based on AI, software integration and quality assurance of computational analysis. Acknowledgement This research was supported by four projects: 1-Two internal projects of the National Research Centre (NRC), Egypt (Project No. 13050503 and No. 13050504); 2- The PRIMA project (2024–2027), No. 47054, entitled “Future-proofing the Mediterranean agri-food chain through integrated and circular management of contaminant-safe water, nutrients and bioresources” (Acronym: MedInCircle); and 3-A scholarship under the TYSP program (No. P19R37009, from December 2024 to December 2025) at the Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China.4-Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia under Grant [KFU250807]. References Ali, R. M., El-Marsafawy, S. M. & Abdeldaym, A. H. Enhancing wheat water productivity using deficit irrigation and genotype selection under arid environments. Agric. Water Manage. 266 , 107570. https://doi.org/10.1016/j.agwat.2022.107570 (2022). Ahmed, M. A., Bastawesy, E., Omar, S. A. & M., & Comparative performance of drip and sprinkler irrigation on wheat yield and water productivity in Egypt. Irrig. Sci. 39 (5), 455–468. https://doi.org/10.1007/s00271-021-00719-1 (2021). El-Khateeb, A. Y. et al. Application of CropWat 8.0 to simulate wheat irrigation requirements and yield response under climate variability. Appl. Water Sci. 13 , 113. 10.1007/s13201-023-02005-3 (2023). https://link.springer.com/article/ Tewabe, D., Abebe, A., Tsige, A., Enyew, A. & Worku, M. Determination of crop water requirements and irrigation scheduling of wheat using CROPWAT at Koga and Rib irrigation schemes, Ethiopia. Indian Journal of Ecology, 48(SI-10), 132–137. (2021). Retrieved from https://www.researchgate.net/publication/353397842 El-Rawy, M., El-Marsafawy, S. M., El-Beltagy, A. E. & El-Kady, M. M. Simulating future irrigation water demands under climate change scenarios in Upper Egypt using CROPWAT model. Appl. Water Sci. 13 (96), 1–12. https://doi.org/10.1007/s13201-023-01961-y (2023). Gabr, M. E. Assessment of irrigation water requirements under climate change scenarios in Egypt using CROPWAT model. Environ. Sci. Pollut. Res. 30 , 3321–3338. https://doi.org/10.1007/s40808-022-01504-2 (2022). Ismail, M. A., Yacoub, R. K., El-Tantawy, M. M. & Abou-Alfotoh, M. S. M. Estimate water requirements of wheat using CROPWAT-8 software – Aswan Governorate – Egypt. Menoufia Journal of Soil Science, 9(4), 31–50. (2024). Retrieved from https://www.researchgate.net/publication/378945678 Kamilaris, A. & Prenafeta-Boldú, F. X. Deep learning in agriculture: A survey. Comput. Electron. Agric. 147 , 70–90. https://doi.org/10.1016/j.compag.2018.02.016 (2018). Liakos, K. G., Busato, P., Moshou, D., Pearson, S. & Bochtis, D. Machine learning in agriculture: A review. Sensors 18 (8), 2674. https://doi.org/10.3390/s18082674 (2018). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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10:33:14","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":175963,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7553639/v1/9bfb70ba624a0a5846527cfb.html"},{"id":93925325,"identity":"97c9742e-b595-41a9-8857-3f0ad7530c65","added_by":"auto","created_at":"2025-10-20 10:25:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":173207,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of irrigation systems, Egyptian varieties and day of planting (Climate change) on observed and simulated wheat Biological yield (Kg/fed).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDAND: Days After Normal Date (Climate change).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7553639/v1/2bee958fb6ca79fd87646fec.png"},{"id":93925538,"identity":"d98be0b0-c08e-4d0e-acdf-58f863a98309","added_by":"auto","created_at":"2025-10-20 10:33:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":118692,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of irrigation systems, Egyptian varieties and day of planting (Climate change) on observed and simulated wheat straw yield (Kg/fed).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNDP: Normal Date of Planting;\u003c/p\u003e\n\u003cp\u003eDAND: Days After Normal Date (Climate change).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7553639/v1/15f625e8b3d72823de2b771a.png"},{"id":93925317,"identity":"4061fc47-c657-4677-a4aa-fa2a4c98aafc","added_by":"auto","created_at":"2025-10-20 10:25:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":110681,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of irrigation systems, Egyptian varieties and day of planting (Climate change) on observed and simulated wheat grain yield (Kg/fed).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNDP: Normal Date of Planting;\u003c/p\u003e\n\u003cp\u003eDAND: Days After Normal Date (Climate change).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7553639/v1/1f9f3efbbca886327037f5be.png"},{"id":93925539,"identity":"37288884-c979-4af8-8ebc-84e026e0c317","added_by":"auto","created_at":"2025-10-20 10:33:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":104058,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of irrigation systems, Egyptian varieties and day of planting (Climate change) on observed and simulated wheat Harvest index.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNDP: Normal Date of Planting;\u003c/p\u003e\n\u003cp\u003eDAND: Days After Normal Date (Climate change).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7553639/v1/1c24e0134e48ba2dc4e15c13.png"},{"id":93925540,"identity":"e682befc-d72f-4a6f-8919-344a8b1c7095","added_by":"auto","created_at":"2025-10-20 10:33:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":83919,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of irrigation systems, Egyptian varieties and day of planting (Climate change) on observed and simulated wheat water productivity (Kg/m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNDP: Normal Date of Planting;\u003c/p\u003e\n\u003cp\u003eDAND: Days After Normal Date (Climate change).\u003c/p\u003e\n\u003cp\u003eWP: Water productivity (kg/m3)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7553639/v1/490c6c4c31326fc4c9cd5c28.png"},{"id":94490579,"identity":"0c7ac163-4914-4911-9126-dd2aed2ac1ab","added_by":"auto","created_at":"2025-10-27 17:12:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2327017,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7553639/v1/9af38bed-16eb-417c-a6d6-fc7a568eb577.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling and Optimizing Wheat Yield under Climate Variability Using Artificial Intelligent and CropWat: A Comparative Study in Nubariya, Egypt","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRecently, machine learning (ML) as a tools of artificial intelligent (AI) have been increasingly integrated into agricultural modeling to capture complex, non-linear interactions among climatic, varietal, and irrigation variables, enhancing decision-support systems.Wheat (\u003cem\u003eTriticum aestivum L.\u003c/em\u003e) is a staple crop in Egypt, playing a crucial role in national food security. However, its cultivation faces significant challenges due to water scarcity and the impacts of climate change, particularly in arid regions like Nubariya. Efficient water management and adaptive agricultural practices are essential to sustain wheat production under these conditions. Climate change has led to increased temperatures and altered precipitation patterns, affecting crop water requirements and yields. Studies have shown that delayed planting dates, as a result of climate variability, can significantly increase the water needs of wheat crops. For instance, research conducted in Aswan Governorate indicated that wheat planted in early November required approximately 4,230 m\u0026sup3;/feddan/season, while planting in early January increased the requirement to about 6,086 m\u0026sup3;/feddan/season \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo address these challenges, simulation models like FAO's CropWat 8.0 have been employed to estimate crop water requirements and optimize irrigation scheduling. CropWat 8.0 utilizes climatic, soil, and crop data to simulate evapotranspiration and irrigation needs, aiding in efficient water resource management. In Egypt, the model has been applied to various crops, including wheat, to define agro ecological zones and manage water resources effectively. The efficiency of water use in wheat farming is influenced by several interrelated factors, including irrigation system type, genetic cultivar differences, and planting date particularly under climate change scenarios that shift rainfall patterns and evapotranspiration rates1. Drip irrigation systems, for example, have been widely reported to improve WP compared to sprinkler or surface irrigation by minimizing evaporation and maximizing water use in the root zone\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn Egypt, the adoption of simulation models such as FAO\u0026rsquo;s CropWat 8.0 provides a valuable tool to estimate crop water requirements and predict WP under diverse conditions. However, discrepancies between observed field data and simulated WP values are common and often result from model limitations in accounting for real-world variability, especially under delayed sowing dates and cultivar-specific responses\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study investigates the combined effects of irrigation systems, three Egyptian wheat cultivars, and planting dates (NDP, 10, 20, and 30 days after normal) on both observed and simulated wheat water productivity, using field trials and CropWat 8.0 simulations in Egypt\u0026rsquo;s arid zones\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe objective is to identify optimal combinations that enhance WP while evaluating the reliability of CropWat as a predictive tool under climatic stress.This study aims to bridge the gap between simulation and reality by assessing the response of different wheat cultivars to various irrigation systems and planting dates under arid conditions in Egypt. By integrating field experiments with CropWat 8.0 simulations, the research seeks to provide insights into optimizing wheat production amidst climate-induced challenges.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eMachine learning predictions demonstrated higher alignment with observed data compared to CropWat in several cases, particularly under stress conditions such as delayed planting and sprinkler irrigation. ML models reduced simulation error and improved varietal responsiveness.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;(1) and Figure (1) shows the Effect of Irrigation Systems, Egyptian Varieties, and Planting Dates (Climate Change) on Observed and Simulated Wheat Biological Yield (kg/fed). The table (1) presents a comparison between observed and simulated biological yield values for wheat under two irrigation systems (sprinkler and drip), three Egyptian varieties (Misr 1, Sakha 95, and Giza 171), and four planting dates (Normal Date of Planting - NDP, and 10, 20, 30 Days After Normal Date - DAND). Each treatment was replicated three times. The simulated values were obtained using the CropWat model. LSD0.05 values were also included to evaluate the statistical significance of differences. Overall, the table shows clear variability between observed and simulated results, indicating that while the CropWat model can generally follow yield trends, it may under- or over-estimate specific values depending on variety, irrigation system, and planting date. The simulated values are consistently lower than the observed values, particularly for the Misr 1 variety. This suggests that the CropWat model may underestimate the impact of sprinkler irrigation on biological yield. Under Drip Irrigation, simulated values under drip irrigation are generally higher or close to observed values, especially at delayed planting dates (20\u0026ndash;30 DAND), indicating a possible overestimation by the model under water-efficient systems. The CropWat model appears to favor drip irrigation outcomes, often producing higher yield estimates than those observed, while underestimating yields under sprinkler irrigation.\u003c/p\u003e\n\u003cp\u003eVariety shows considerable differences between observed and simulated values under sprinkler irrigation. Under drip irrigation, simulated values approach or exceed observed ones. Sakha 95, Exhibits the greatest variation between observed and simulated yields across both irrigation systems, suggesting that the model does not reliably predict performance for this variety. Giza 171, Demonstrates strong consistency between observed and simulated values, particularly under drip irrigation. CropWat predictions align well with real-world data for this variety. The Giza 171 variety shows the best alignment between model and observed yield, while Sakha 95 reflects inconsistencies, suggesting a need for better calibration for certain varieties in the model.\u003c/p\u003e\n\u003cp\u003eEffect of Planting Date (Climate Change), both observed and simulated yields demonstrate a declining trend as planting is delayed from the NDP to 30 DAND. However, the decline is steeper and more pronounced in observed values than in simulated ones, indicating that CropWat may underrepresent the adverse effects of delayed planting. This suggests that the model may not fully account for the interplay between climate change and planting date in its simulations. CropWat recognizes the general effect of planting date, but tends to smooth over yield reductions associated with delayed sowing, possibly leading to optimistic predictions under climate stress.\u003c/p\u003e\n\u003cp\u003eSignificance of Differences (LSD 0.05), LSD0.05 values range from 61.05 to 173.68 kg/fed, setting thresholds for statistical significance. Many observed-simulated differences exceed LSD thresholds, indicating statistically significant discrepancies. The statistical analysis confirms that many discrepancies between observed and simulated values are not due to random variation but reflect meaningful model limitations. The CropWat model can be a useful predictive tool for wheat yield under varying irrigation systems and planting schedules. However, this analysis highlights that: The model tends to underestimate yields under sprinkler irrigation and overestimate under drip irrigation. CropWat\u0026rsquo;s performance varies by variety, with Giza 171 being best represented. The model does not accurately reflect yield reductions caused by delayed planting, likely due to limited climate sensitivity. Several discrepancies are statistically significant, requiring model recalibration or the inclusion of more dynamic climate and variety parameters.\u003c/p\u003e\n\u003cp\u003eTable (1) Effect of irrigation systems, Egyptian varieties and day of planting (Climate change) on observed and simulated wheat Biological yield (Kg/fed).\u003c/p\u003e\n\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eFactors and treatments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eObserved\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eSimulated by CropWat Model\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIrrigation System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRep.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioYield NDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioYield 10 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioYield 20 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioYield 30 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioYield Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioYield NDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioYield\u003c/p\u003e\n \u003cp\u003e10 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioYield\u003c/p\u003e\n \u003cp\u003e20 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioYield\u003c/p\u003e\n \u003cp\u003e30 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioYield Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003eSprinkler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMisr 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7571.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7246.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6956.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7650.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7139.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7419.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6386.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6195.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7321.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8259.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8610.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6642.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7217.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7888.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7244.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7279.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6481.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7267.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8248.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6960.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8000.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6508.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6731.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7278.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7257.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7343.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6512.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7087.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6562.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7387.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSakha 95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6928.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5743.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6271.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6769.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6998.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8064.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6512.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7417.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7845.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5685.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7032.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6491.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6850.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7298.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6300.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6470.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5999.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7305.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7531.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5929.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7044.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6378.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6775.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6658.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6597.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8015.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6884.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6591.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7087.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6293.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGiza 171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7991.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7306.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8180.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8376.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7745.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7663.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7468.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6934.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7275.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7802.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7841.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6741.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7236.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8300.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8143.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8766.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6501.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7211.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7649.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8524.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8143.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6685.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7675.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8251.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8183.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8058.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6162.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7951.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8491.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7582.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003eDrip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMisr 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8395.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7593.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7375.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7953.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8155.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7996.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6095.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7119.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8580.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8209.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8276.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7336.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7198.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7719.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7299.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7316.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7231.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6871.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6849.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7976.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8423.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7491.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7750.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8660.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7558.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8265.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6547.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7263.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8709.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7629.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSakha 95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8206.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6075.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7266.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7621.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7381.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6828.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6445.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6693.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7074.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7358.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7241.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6169.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6796.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7472.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7399.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8578.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6108.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7503.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7501.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7261.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7776.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6590.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6535.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7571.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7276.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8233.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5774.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5727.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7708.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6565.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGiza 171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8338.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7422.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7729.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8397.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8449.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9244.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6237.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8603.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9218.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8288.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9125.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7636.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7697.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8454.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7891.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7338.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7351.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8112.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7021.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7640.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8573.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6827.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8268.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8714.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8072.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9223.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6307.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7058.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8541.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8328.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eLSD 0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e69.19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e61.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e75.14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e78.86\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e120.96\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e105.95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e150.83\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e173.68\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e132.99\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003eNDP: Normal Date of Planting;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eDAND: Days After Normal Date (Climate change).\u003c/p\u003e\n\u003cp\u003eAnd Figure\u003c/p\u003e\n\u003cp\u003eDAND: Days After Normal Date (Climate change).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;(2) and Figure (2) compares biological yield (Kg/fed), data obtained through field observation with data simulated by the CropWat model. The treatments involved two irrigation systems (sprinkler and drip), three wheat varieties (Misr 1, Sakha 95, and Giza 171), and four planting dates: the normal date of planting (NDP), and 10, 20, and 30 days after the normal date (DAND), representing climatic delay. Each treatment was replicated three times. The LSD0.05 values serve as a reference for statistical significance of the differences.\u003c/p\u003e\n\u003cp\u003eThe table reveals variability in the model\u0026rsquo;s accuracy across systems, varieties, and planting delays, reflecting the model\u0026rsquo;s partial capability to predict straw yield performance under changing climatic and management conditions.\u003c/p\u003e\n\u003cp\u003eSimulated values tend to be lower or inconsistent when compared to observed data. In several cases, the simulated values do not reflect the magnitude of straw biomass production, especially for Misr 1 and Sakha 95. Simulated values are sometimes higher than observed, indicating a potential overestimation by CropWat under water-efficient systems. However, variability is still present, and the model does not consistently predict straw yield behavior.\u003c/p\u003e\n\u003cp\u003eThe observed values show a clear trend of decreasing straw yield with delayed planting.\u003c/p\u003e\n\u003cp\u003eSimulated values follow a general decline but do not replicate the fluctuations between replicates effectively. For example, at 30 DAND, observed values vary between 4099.5 and 4591.8, while simulated values remain clustered. A significant mismatch is observed between actual and simulated values across replicates and planting dates. For instance, Rep 3 (30 DAND): Observed\u0026thinsp;=\u0026thinsp;3957.16 kg/fed, Simulated\u0026thinsp;=\u0026thinsp;3582.75 kg/fed \u0026ndash; a large difference with possible statistical significance (LSD\u0026thinsp;=\u0026thinsp;102.49). Simulated values for Giza 171 are more consistent, but they still differ from observed data, especially at the later planting dates (20\u0026ndash;30 DAND).\u003c/p\u003e\n\u003cp\u003eEffect of Planting Date (Climate Change)\u003c/p\u003e\n\u003cp\u003eAcross all varieties and systems, a general decline in straw yield is observed as planting is delayed from the normal date to 30 DAND. The simulated values follow the same general trend, but they often fail to replicate the exact rate of decline or the within-replicate variability. This could be due to the limited ability of the model to integrate short-term climate variability and its physiological effects on biomass accumulation.\u003c/p\u003e\n\u003cp\u003eStatistical Significance (LSD 0.05): The LSD values vary from 61.99 to 181.02 kg/fed, indicating that differences between observed and simulated values exceeding these thresholds are statistically significant. Several comparisons exceed LSD, especially under sprinkler irrigation and with delayed planting, confirming that CropWat predictions may not be statistically reliable in many conditions.\u003c/p\u003e\n\u003cp\u003eThe CropWat model provides a broad approximation of straw yield in response to irrigation, genotype, and planting date variations. However:\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eIt shows inconsistency across replicates and often under- or overestimates straw yield, especially under sprinkler irrigation and for Sakha 95.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe model\u0026rsquo;s response to climate-related planting delays is correct in direction but insufficient in magnitude.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe statistical analysis (LSD) confirms that many differences are significant, emphasizing the need for model refinement and calibration to improve prediction accuracy for straw yield under variable field conditions.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eTable (2) Effect of irrigation systems, Egyptian varieties and day of planting (Climate change) on observed and simulated wheat straw yield (Kg/fed).\u003c/div\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eFactors and treatments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eObserved\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eSimulated by CropWat Model\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIrrigation System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRep.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStraw Yield ND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStraw Yield\u003c/p\u003e\n \u003cp\u003e10 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStraw Yield\u003c/p\u003e\n \u003cp\u003e20 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStraw Yield\u003c/p\u003e\n \u003cp\u003e30 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStraw Yield\u003c/p\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStraw Yield ND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStraw Yield\u003c/p\u003e\n \u003cp\u003e10 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStraw Yield\u003c/p\u003e\n \u003cp\u003e20 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStraw Yield\u003c/p\u003e\n \u003cp\u003e30 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStraw Yield Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003eSprinkler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMisr 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5536.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4989.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4619.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4099.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5168.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4697.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4722.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4090.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4501.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5732.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5029.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4711.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4175.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4494.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5613.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5274.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4578.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4535.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4151.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5230.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4473.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4541.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4591.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4637.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4526.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4029.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4527.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3704.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4450.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSakha 95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4811.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4545.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4539.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4114.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4262.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4175.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4724.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4818.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3385.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4332.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4435.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4915.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4630.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4163.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4249.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4457.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4122.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4178.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3423.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4592.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4566.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4805.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4570.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3957.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4322.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3818.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4569.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4529.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3582.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4712.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGiza 171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5549.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4967.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4749.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4455.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5322.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6001.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5683.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5223.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4423.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4389.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5433.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5189.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5281.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4828.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4991.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4522.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4539.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5583.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4988.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4502.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5523.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5291.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4768.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4976.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5105.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4783.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4928.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4683.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5206.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5414.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003eDrip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMisr 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5675.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4761.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4601.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4412.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5104.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4901.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4464.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4927.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4317.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5119.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5686.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4483.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4560.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4428.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4764.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6098.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4177.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4717.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4662.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4456.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5234.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4497.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4902.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4846.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5129.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4872.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5017.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4647.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4571.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4711.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSakha 95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4972.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4662.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4509.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4196.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4686.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4598.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4791.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3961.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3710.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4166.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4823.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4526.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4731.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4263.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4647.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4261.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4695.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4954.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4079.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4818.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5070.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4742.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4659.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4066.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4749.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4966.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4862.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4403.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4894.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGiza 171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5804.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5338.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5389.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5219.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5657.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5006.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4631.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5062.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4347.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5667.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5477.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5390.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5313.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5063.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5253.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4976.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6012.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4991.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5105.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4783.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5945.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5539.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5545.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5139.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5045.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5529.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4879.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4943.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4502.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5299.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eLSD 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNDP: Normal Date of Planting;\u003c/p\u003e\n\u003cp\u003eDAND: Days After Normal Date (Climate change).\u003c/p\u003e\n\u003cp\u003eNDP: Normal Date of Planting;\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;(3) and Figure (3) presents a side-by-side comparison of field-observed and CropWat-simulated grain yield values across two irrigation systems (sprinkler and drip), three wheat varieties (Misr 1, Sakha 95, Giza 171), and four sowing dates: the normal date of planting (NDP), and 10, 20, and 30 days after normal planting date (DAND). Each treatment was replicated three times. LSD0.05 values indicate the least significant differences necessary to consider yield differences statistically meaningful.\u003c/p\u003e\n\u003cp\u003eThe data reflect important insights into the accuracy and limitations of the CropWat model in simulating grain yield, especially under conditions of climate-related planting delays and different irrigation strategies.\u003c/p\u003e\n\u003cp\u003eSimulated values often deviate significantly from observed values. In many cases, the model underestimates the actual grain yield. The variability across replicates is not well captured by the simulation, especially under later DAND scenarios.\u003c/p\u003e\n\u003cp\u003eUnder drip irrigation, simulated values sometimes overestimate yield at NDP (e.g., Giza 171), while underestimating it at later DAND stages (e.g., Sakha 95).\u003c/p\u003e\n\u003cp\u003eFor instance, Giza 171 (Rep 1, NDP): Observed\u0026thinsp;=\u0026thinsp;3147.28 vs. Simulated\u0026thinsp;=\u0026thinsp;2980.23 \u0026rarr; close and within LSD margin\u0026thinsp;=\u0026thinsp;161.36 \u0026rArr; not significant. However, the model tends to flatten variability, reducing accuracy across delayed planting dates.\u003c/p\u003e\n\u003cp\u003eCropWat demonstrates greater prediction consistency under drip irrigation, yet underperforms under sprinkler systems, where environmental variability may have a greater influence on yield dynamics.\u003c/p\u003e\n\u003cp\u003eThe simulated grain yields for Misr 1 often fall below the observed values, particularly in early planting stages. Variability between replicates in the observed data is not well replicated by the model, indicating a lack of sensitivity to intra-treatment variation. Demonstrates significant differences between observed and simulated values. The model struggles to reflect grain yield response under stress conditions for this variety. Overall, Giza 171 shows better agreement between observed and simulated values, though inconsistencies remain at certain planting dates. The model performs most accurately for Giza 171, while Misr 1 and Sakha 95 show substantial under- or over-predictions, particularly at later planting dates and under sprinkler irrigation.\u003c/p\u003e\n\u003cp\u003eImpact of Planting Date (Climate Change), Observed grain yields consistently decline as the planting date is delayed beyond NDP, aligning with expected impacts of climate change (reduced growth periods, stress exposure). Simulated yields generally follow the same trend, but the magnitude of decline is not accurately represented in many cases. The model tends to produce moderate or stable declines, which may mask the severity of climate-induced yield losses captured in field data. For example, Sakha 95 (Drip \u0026ndash; Rep 2): Observed yield drops from 2573.05 at NDP to 2189.48 at 30 DAND, while simulated yield decreases from 2337.99 to 2066.60\u0026mdash;less steep. While CropWat reflects the direction of change due to planting delay, it underestimates the magnitude of the climatic impact on grain yield. The LSD values range from 57.49 to 190.74, indicating thresholds for significance. Many of the differences between observed and simulated values exceed LSD thresholds, confirming statistically significant deviations. The statistical analysis indicates that several of the simulated results are not only different from observed values, but significantly so, particularly under planting delays and with more sensitive varieties.\u003c/p\u003e\n\u003cp\u003eThe CropWat model performs inconsistently in simulating wheat grain yield, particularly under sprinkler irrigation and climate-induced planting delays. It is more reliable for the Giza 171 variety and under drip irrigation. The model generally captures trends, but fails to accurately replicate magnitudes and inter-replicate variability. Many differences between observed and simulated values are statistically significant, emphasizing the need for further model calibration, especially to account for variety-specific responses and climate stress interactions.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;(3) Effect of irrigation systems, Egyptian varieties and day of planting (Climate change) on observed and simulated wheat grain yield (Kg/fed).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eFactors and treatments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eObserved\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eSimulated by CropWat Model\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIrrigation System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRep.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrian Yield-ND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrian Yield 10 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrian Yield 20 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrian Yield 30 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrian Yield Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrian Yield-ND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrian Yield-10 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrian Yield-20 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrian Yield-30 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrian Yield-Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003eSprinkler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMisr 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2744.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2580.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2579.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2457.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2621.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2531.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2877.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2188.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2256.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2260.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2641.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2863.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2637.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2426.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2421.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2514.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2391.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2636.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2420.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2447.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2877.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2606.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2547.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2516.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2489.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2915.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2256.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2431.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2607.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2822.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSakha 95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2602.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2544.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2375.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2283.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2501.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2719.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2491.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2252.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2121.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2098.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2382.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2341.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2337.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2113.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2504.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2826.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2438.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2543.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2239.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2144.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2516.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2463.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2424.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2100.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2389.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2520.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2115.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2485.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1932.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2171.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGiza 171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2680.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2559.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2489.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2418.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2796.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3209.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2289.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2318.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2311.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2747.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2691.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2915.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2529.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2396.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2724.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2474.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2686.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2366.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2569.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2831.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3060.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2828.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2370.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2262.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2543.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2548.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2356.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2197.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2295.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2882.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003eDrip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMisr 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3008.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2823.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2520.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2498.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2822.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2728.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2932.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2443.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2624.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2293.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2874.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2769.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2566.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2343.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2605.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3013.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2670.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2366.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2310.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2278.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3033.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3050.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2570.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2642.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2690.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3184.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2938.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2544.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2153.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2563.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSakha 95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2781.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2563.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2555.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2160.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2560.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2294.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2199.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2622.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2226.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2725.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2573.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2569.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2479.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2189.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2368.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2337.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2621.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2155.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2411.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2066.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2531.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2434.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2535.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2364.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2527.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2897.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2731.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2679.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2024.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2327.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGiza 171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3147.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3048.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2357.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2376.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2695.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2980.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2996.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2364.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2537.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2665.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2925.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3009.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2411.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2563.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2728.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3195.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2637.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2060.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2230.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3014.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2764.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2849.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2619.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2509.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2539.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2534.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2847.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2343.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2509.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2440.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eLSD 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003eNDP: Normal Date of Planting;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eDAND: Days After Normal Date (Climate change).\u003c/p\u003e\n\u003cp\u003eNDP: Normal Date of Planting;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;(4) and Figure (4) compares the observed and simulated (CropWat model) values of the harvest index (HI) under two irrigation systems (sprinkler and drip), three wheat varieties (Misr 1, Sakha 95, and Giza 171), and four planting dates: the normal date of planting (NDP), and 10, 20, and 30 days after the normal date (DAND). Each treatment includes three replicates, and LSD0.05 values are provided to assess the statistical significance of differences.\u003c/p\u003e\n\u003cp\u003eThe harvest index, representing the ratio of grain yield to biological yield, is a critical indicator of crop efficiency and productivity under changing climatic and management conditions. Simulated HI values tend to overestimate the observed values in most cases, particularly for the variety Misr 1. However, in many cases, variability is poorly represented in the simulation, especially with delayed planting. under drip irrigation, simulated values show inconsistent patterns, some replicates are higher, others lower than the observed. In general, the model flattens the variation, which is more pronounced in observed data.\u003c/p\u003e\n\u003cp\u003eThe CropWat model shows limited accuracy in simulating HI under different irrigation systems, particularly when climatic variation or planting delay is introduced.\u003c/p\u003e\n\u003cp\u003eThe observed HI values for Misr 1 tend to be higher than simulated values under drip irrigation and more aligned under sprinkler irrigation. Simulated HI values often underestimate the observed ones. Giza 171 shows more variable performance, with simulated values sometimes lower or higher than observed. Rep 3 (Drip \u0026ndash; 10 DAND): Observed\u0026thinsp;=\u0026thinsp;37.27 vs. Simulated\u0026thinsp;=\u0026thinsp;37.68 \u0026rarr; close agreement. The harvest index simulation is most consistent for Giza 171, while Misr 1 and Sakha 95 show irregular deviations, depending on the irrigation type and planting date.\u003c/p\u003e\n\u003cp\u003eEffect of Planting Date (Climate Change)\u003c/p\u003e\n\u003cp\u003eObserved HI values generally decline with delayed planting, reflecting stress and shortened grain-filling duration due to climate factors. Simulated values do not always reflect this trend and sometimes show stable or increasing HI values, particularly for Misr 1 and Sakha 95, indicating overestimation of crop efficiency under stress conditions.\u003c/p\u003e\n\u003cp\u003eCropWat captures the general trend of reduced HI under planting delay, but the magnitude and direction vary by variety and irrigation system, reducing prediction confidence.\u003c/p\u003e\n\u003cp\u003e5. Statistical Significance (LSD 0.05), The LSD values are very high in some columns (e.g., \u0026gt;\u0026thinsp;160), likely due to high within-treatment variability or model uncertainty. As a result, even substantial numeric differences may not be statistically significant, making interpretation cautious. However, some differences, especially those exceeding 5\u0026ndash;7 points in HI, may still indicate biological importance even if not statistically significant. The large LSD values weaken the statistical interpretation, but biological relevance must still be considered when evaluating HI differences between observed and simulated data.\u003c/p\u003e\n\u003cp\u003eThe analysis of Table (4) reveals that: The CropWat model lacks precision in simulating harvest index, particularly under delayed planting and irrigation changes. It tends to overestimate HI in stressful conditions, possibly due to limitations in modeling physiological responses (e.g., grain filling). The performance is moderately acceptable for Giza 171, but less reliable for Misr 1 and Sakha 95. The high LSD values limit the confidence in significance testing but highlight the need for model refinement to better capture HI variability across conditions.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;(4) Effect of irrigation systems, Egyptian varieties and day of planting (Climate change) on observed and simulated wheat Harvest index.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tabd\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eFactors and treatments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eObserved\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eSimulated by CropWat\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIrrigation System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRep.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHI\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHI\u003c/p\u003e\n \u003cp\u003e10 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHI\u003c/p\u003e\n \u003cp\u003e20 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHI\u003c/p\u003e\n \u003cp\u003e30 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHI Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHI-Normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHI\u003c/p\u003e\n \u003cp\u003e10 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHI\u003c/p\u003e\n \u003cp\u003e20 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHI\u003c/p\u003e\n \u003cp\u003e30 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHI\u003c/p\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003eSprinkler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMisr 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSakha 95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGiza 171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003eDrip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMisr 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSakha 95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGiza 171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSD 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e169.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e169.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003eNDP: Normal Date of Planting;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eDAND: Days After Normal Date (Climate change).\u003c/p\u003e\n\u003cp\u003eHI: Harvest index\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;(5) Effect of irrigation systems, Egyptian varieties and day of planting (Climate change) on observed and simulated wheat water productivity (Kg/m\u003csup\u003e3\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tabe\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eFactors and treatments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eObserved\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eSimulated by CropWat Model\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIrrigation System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRep.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater Applied\u003c/p\u003e\n \u003cp\u003e(m3/fed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP-ND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP\u003c/p\u003e\n \u003cp\u003e10-DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP\u003c/p\u003e\n \u003cp\u003e20 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP\u003c/p\u003e\n \u003cp\u003e30 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP\u003c/p\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP\u003c/p\u003e\n \u003cp\u003e10 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP\u003c/p\u003e\n \u003cp\u003e20 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP\u003c/p\u003e\n \u003cp\u003e30 DAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWP\u003c/p\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003eSprinkler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMisr 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003e4563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSakha 95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGiza 171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003eDrip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMisr 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003e1852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSakha 95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGiza 171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eLSD 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\"\u003eNDP: Normal Date of Planting;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eDAND: Days After Normal Date (Climate change).\u003c/p\u003e\n\u003cp\u003eWP: Water productivity (kg/m3)\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;(5) and Figure (5) presents water productivity (WP) values (kg of grain per cubic meter of water) as observed in the field and simulated by the CropWat model, under: Two irrigation systems (sprinkler and drip), Three Egyptian wheat varieties (Misr 1, Sakha 95, and Giza 171), Four planting dates: Normal Date of Planting (NDP) and 10, 20, and 30 Days After Normal Date (DAND), and Each treatment replicated three times. LSD0.05 values are provided to evaluate statistical significance between observed and simulated means under each condition. Water productivity is a key metric of irrigation efficiency, especially under water-scarce and climate-vulnerable conditions.\u003c/p\u003e\n\u003cp\u003eObserved WP values are lower than under drip irrigation, typically ranging from ~\u0026thinsp;0.47 to 0.65 kg/m\u0026sup3;. CropWat simulations sometimes overestimate WP, especially under early planting (NDP and 10-DAND). Observed WP is significantly higher, ranging between 1.15 to 1.69 kg/m\u0026sup3;, aligning with expectations of drip systems offering high water-use efficiency. Simulated values often underestimate WP, especially under later planting dates (20 and 30 DAND). The model captures the relative advantage of drip over sprinkler irrigation, but it underestimates WP under stress (late sowing) and sometimes overestimates under ideal conditions, leading to inconsistencies. Demonstrates consistent WP across planting dates with slight variation. Simulated WP values vary more than observed ones, suggesting model sensitivity may not reflect biological stability of this variety. Sakha 95: Observed WP under sprinkler irrigation is low (~\u0026thinsp;0.50), while under drip, WP values are relatively high (~\u0026thinsp;1.15\u0026ndash;1.53). Simulated values for this variety are frequently lower than observed, especially under drip irrigation, indicating underrepresentation of Sakha 95\u0026rsquo;s water-use efficiency in the model. Giza 171: Observed values reflect the most stable and efficient water use, especially under drip irrigation. Simulated values for Giza 171 are more variable, failing to consistently reflect its superior observed performance. CropWat shows limited varietal discrimination, particularly underestimating WP for Sakha 95 and Giza 171 in several instances.\u003c/p\u003e\n\u003cp\u003eEffect of Planting Date (Climate Change): Observed data confirms that water productivity declines with planting delay, especially under sprinkler irrigation, due to reduced grain yield and possible higher evapotranspiration. CropWat simulations follow a similar declining trend, but the rate of decline is often inconsistent or less pronounced, particularly under drip irrigation. For instance, Misr 1 (Drip \u0026ndash; Rep 1): Observed WP decreases from 1.62 (NDP) to 1.28 (30 DAND) \u0026rarr; 21% decline; Simulated: from 1.47 to 1.36 \u0026rarr; only 7.5% decline. While CropWat captures the general impact of delayed planting on WP, it underestimates the extent of climate-induced efficiency losses, particularly for high-performing varieties. LSD values range between 0.062 and 0.7381, depending on the planting date and condition. Numerous differences between observed and simulated WP exceed the LSD threshold, indicating statistically significant discrepancies. Example: Sakha 95 (Sprinkler \u0026ndash; Rep 1, 20 DAND): Observed\u0026thinsp;=\u0026thinsp;0.51 vs. Simulated\u0026thinsp;=\u0026thinsp;0.42 \u0026rarr; difference\u0026thinsp;=\u0026thinsp;0.09\u0026thinsp;\u0026gt;\u0026thinsp;LSD\u0026thinsp;=\u0026thinsp;0.12355 \u0026rarr; significant. Statistically significant differences are frequent, especially under late planting and drip systems, indicating areas where CropWat requires recalibration for WP simulation.Observed water productivity is highest under drip irrigation, with consistent superiority across all varieties and planting dates. Simulated WP values generally align with observed trends but often under- or overestimate actual values, depending on the variety and planting time. Statistical testing (LSD) highlights several significant differences, particularly under stress conditions (30 DAND), suggesting the need for model improvement in dynamic climatic scenarios. Enhance CropWat\u0026rsquo;s sensitivity to variety-specific water-use traits. Improve model algorithms to better reflect stress impacts under climate change. Utilize field-calibrated WP coefficients to align simulations with observed values.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe inclusion of machine learning allowed for a more adaptive modeling approach, accommodating field-specific variability and enhancing model sensitivity to genotype and climate delay interactions. This integration showed promise in refining traditional simulation frameworks such as CropWat\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe comparison between observed and simulated wheat performance across different irrigation systems, cultivars, and planting dates reveals important insights into the predictive capacity and limitations of the \u003cb\u003eCropWat 8.0\u003c/b\u003e model. Observed biological yields exceeded simulated values under \u003cb\u003esprinkler irrigation\u003c/b\u003e, particularly for \u003cem\u003eMisr 1\u003c/em\u003e. This suggests the model's underestimation of vegetative growth under conditions of climatic stress and irrigation inefficiency. Under \u003cb\u003edrip irrigation\u003c/b\u003e, simulated values were more aligned with observed data, especially for \u003cem\u003eGiza 171\u003c/em\u003e, indicating that the model performs better under uniform and efficient water application\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eStraw yield simulations varied significantly between irrigation systems and cultivars. The model tended to \u003cb\u003eoverestimate straw yield under drip irrigation\u003c/b\u003e, particularly for \u003cem\u003eSakha 95\u003c/em\u003e, and \u003cb\u003eunderestimate under sprinkler\u003c/b\u003e, especially with late planting. These discrepancies were statistically significant in several cases, exceeding LSD thresholds, and highlight the limited responsiveness of the model to vegetative biomass accumulation dynamics. Grain yield decreased consistently with delayed sowing (NDP to 30 DAND). While CropWat simulated the general declining trend, it failed to reflect the \u003cb\u003efull magnitude of yield loss\u003c/b\u003e, particularly for \u003cem\u003eMisr 1\u003c/em\u003e and \u003cem\u003eSakha 95\u003c/em\u003e. The model also flattened variability across replicates, indicating weak sensitivity to intra-treatment differences and planting stress\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHarvest Index (HI) values derived from simulations were inconsistent, especially under \u003cb\u003elate planting\u003c/b\u003e. Observed HI declined with delayed planting, but the model often produced stable or inflated HI values. This discrepancy highlights the limited physiological modeling capacity of CropWat when simulating source-sink dynamics under stress\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Water productivity was highest under \u003cb\u003edrip irrigation\u003c/b\u003e, confirming the system's efficiency in maximizing grain yield per unit of water. CropWat tended to \u003cb\u003eoverestimate WP at early planting\u003c/b\u003e and \u003cb\u003eunderestimate it under delayed sowing\u003c/b\u003e, especially for \u003cem\u003eSakha 95\u003c/em\u003e. This mismatch between simulated and observed WP emphasizes the model\u0026rsquo;s limitation in capturing the compound effects of water stress and shortened growth duration \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOverall, CropWat 8.0 provided a \u003cb\u003ereasonable approximation\u003c/b\u003e of yield and water-use efficiency trends under varied conditions. However, substantial differences in magnitude and statistical significance call for \u003cb\u003elocal calibration\u003c/b\u003e and \u003cb\u003eintegration of genotype-specific parameters\u003c/b\u003e to enhance the model's predictive validity under climate change scenarios \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eIn this study, machine learning models specifically regression-based algorithms were utilized alongside CropWat 8.0 to improve simulation accuracy. These models were trained on field-observed datasets to predict key output parameters including biological yield, grain yield, straw yield, and water productivity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Experimental Site\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe field experiments were conducted at the Experimental Research Farm of the National Research Centre (NRC), located in Nubariya, Beheira Governorate, Egypt (Latitude: 30.86\u0026deg; N, Longitude: 30.27\u0026deg; E). The area is characterized by arid climatic conditions, sandy loam soil, and limited rainfall, making it representative of water-scarce agricultural regions in Egypt.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Experimental Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA split-split plot design was implemented with three replications:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eMain plots\u003c/strong\u003e: Two irrigation systems \u0026ndash; \u003cem\u003esprinkler irrigation\u003c/em\u003e and \u003cem\u003edrip irrigation\u003c/em\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eSub-plots\u003c/strong\u003e: Three Egyptian wheat (\u003cem\u003eTriticum aestivum L.\u003c/em\u003e) cultivars \u0026ndash; \u003cem\u003eMisr 1\u003c/em\u003e, \u003cem\u003eSakha 95\u003c/em\u003e, and \u003cem\u003eGiza 171\u003c/em\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eSub-sub-plots\u003c/strong\u003e: Four sowing dates \u0026ndash; Normal Date of Planting (NDP), and 10, 20, and 30 Days After Normal Date (DAND) to simulate the impact of planting delay under climate change scenarios.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e3. Field Management\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStandard agronomic practices were applied uniformly across all treatments, including fertilization, weed and pest control, and land preparation. Irrigation was applied according to the treatment schedule using flow meters to ensure accurate water application monitoring.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following parameters were measured:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eBiological yield\u003c/strong\u003e (kg/fed)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGrain yield\u003c/strong\u003e (kg/fed)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eStraw yield\u003c/strong\u003e (kg/fed)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eHarvest Index (HI)\u003c/strong\u003e: calculated as grain yield / biological yield \u0026times; 100\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eWater Productivity (WP)\u003c/strong\u003e: calculated as grain yield / irrigation water applied (kg/m\u0026sup3;)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e5. CropWat Model Application\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eThe CropWat 8.0 model was used to simulate the same parameters under the defined treatments.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo simulate wheat water requirements, biomass production, and yield response under varying field conditions, the \u003cstrong\u003eCropWat 8.0 model\u003c/strong\u003e, developed by the FAO, was used as a supporting analytical tool. This model is widely recognized for its ability to estimate crop evapotranspiration, irrigation requirements, and yield reductions due to water stress under different climatic and agronomic scenarios.\u003c/p\u003e\n\u003cp\u003eIn this study, \u003cstrong\u003eCropWat 8.0\u003c/strong\u003e was applied using field-based climatic inputs (temperature, humidity, wind speed, sunshine hours), soil characteristics (texture, infiltration rate, field capacity), and crop-specific data for wheat growth stages. Key parameters included:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCrop coefficients (Kc) for wheat at each stage\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eYield response factor (Ky)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRoot depth and depletion fraction\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePlanting dates (Normal and 10, 20, 30 days delayed)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe model simulated biological yield, grain yield, straw yield, harvest index (HI), and water productivity (WP), and outputs were compared with observed field data for calibration and validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCropWat 8.0 has been effectively used in previous research in Egypt and globally\u003c/strong\u003e to assess water requirements and optimize irrigation management for wheat:\u003c/p\u003e\n\u003cp\u003eDespite its strengths, \u003cstrong\u003eCropWat 8.0 remains a static model\u003c/strong\u003e and may require field-specific calibration for genotype-specific simulations and dynamic climate responses.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eClimatic data, crop characteristics, and soil properties were input based on local conditions and FAO guidelines.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e6. Integration of Machine Learning Approaches\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo enhance the predictive accuracy of wheat yield and water productivity simulations under climate variability, machine learning (ML) techniques were integrated alongside the traditional CropWat 8.0 model. The ML component focused on capturing non-linear relationships and complex interactions between irrigation system type, planting dates, and genotype-specific responses, which are often oversimplified in rule-based models.\u003c/p\u003e\n\u003cp\u003eField-observed data from all treatments\u0026mdash;including biological yield, grain yield, straw yield, harvest index, and water productivity\u0026mdash;were compiled and preprocessed for ML training. Feature variables included categorical (e.g., irrigation type, wheat variety) and continuous (e.g., sowing delay, climatic inputs) parameters. The dataset was split into 70% for training and 30% for validation.\u003c/p\u003e\n\u003cp\u003eMultiple regression-based ML algorithms were tested, including:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRandom Forest Regressor\u003c/li\u003e\n\u003cli\u003eSupport Vector Regression (SVR)\u003c/li\u003e\n\u003cli\u003eGradient Boosting Regressor\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n\u003cp\u003eModel performance was evaluated using standard statistical metrics such as Root Mean Square Error (RMSE), R-squared (R\u0026sup2;), and Mean Absolute Error (MAE). The best-performing models were selected to predict each target variable across all experimental treatments. These predictions were then compared to both the CropWat outputs and field-observed data.\u003c/p\u003e\n\u003cp\u003eThis ML-assisted simulation allowed for enhanced calibration and validation of the CropWat model by highlighting systematic deviations and enabling post-hoc correction factors, particularly for underrepresented stress conditions (e.g., delayed planting under sprinkler irrigation). The integration of ML into this workflow contributed to a more adaptive and precise tool for irrigation planning and cultivar selection under climate-induced variability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eData were analyzed using ANOVA to assess the significance of main effects and interactions.\u003c/li\u003e\n\u003cli\u003eLeast Significant Difference (LSD0.05) values were calculated to determine significant differences between means.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe combined use of CropWat and machine learning approaches is recommended to increase the robustness and accuracy of crop modeling under climate variability, supporting informed agricultural management decisions. Based on the findings of this study, the following recommendations are proposed to enhance wheat productivity and improve simulation accuracy under climate-induced stress: Adopt drip irrigation systems as a preferred method in arid regions, given their superior performance in maximizing water productivity and yield stability, particularly for the \u003cem\u003eGiza 171\u003c/em\u003e variety. Optimize planting dates by avoiding delays beyond 10 days after the normal sowing window, as significant yield and water productivity losses were observed with late planting under both irrigation systems. Calibrate the CropWat 8.0 model using localized field data for each variety, especially \u003cem\u003eSakha 95\u003c/em\u003e and \u003cem\u003eMisr 1\u003c/em\u003e, which exhibited poor simulation accuracy under stress conditions. Incorporate dynamic climate-response parameters into the CropWat framework to better simulate short-term temperature fluctuations and their effects on wheat development and yield. Promote integrated use of simulation tools with field validation to support decision-making in irrigation planning and crop management under climate change scenarios.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor ContributionsMaher Fathy Attia Morsy: Conceptualization, design of field experiment, data collection, CropWat simulations, analysis of data and writing the manuscript.Hani A. Mansour: Methodology development, irrigation system supervision, statistical analysis, interpretation of results, and manuscript review and editing.Mohamed Abd El-Hady: Field experiment coordination, crop management and verification of observed data with simulated outputs.Li Qian: Predictive models under climate variability: machine learning modeling, algorithm implementation, and optimization.Mohamed M. Ibrahim: Technical assistance in the sphere of agriculture engineering, as irrigation equipment, performance monitoring.Lamy M. Hamed: Vital contribution to soil and water data analysis and interpretation of the water productivity outcomes and critical review of the discussion.Yang, and Hua Wei: Will provide knowledge in optimization methods based on AI, software integration and quality assurance of computational analysis.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis research was supported by four projects: 1-Two internal projects of the National Research Centre (NRC), Egypt (Project No. 13050503 and No. 13050504); 2- The PRIMA project (2024\u0026ndash;2027), No. 47054, entitled \u0026ldquo;Future-proofing the Mediterranean agri-food chain through integrated and circular management of contaminant-safe water, nutrients and bioresources\u0026rdquo; (Acronym: MedInCircle); and 3-A scholarship under the TYSP program (No. P19R37009, from December 2024 to December 2025) at the Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China.4-Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia under Grant [KFU250807].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAli, R. 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Machine learning in agriculture: A review. \u003cem\u003eSensors\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e (8), 2674. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/s18082674\u003c/span\u003e\u003cspan address=\"10.3390/s18082674\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Wheat, Water Productivity, Irrigation Systems, CropWat 8.0, Artificial intelligent, Planting Date Delay, Climate Change","lastPublishedDoi":"10.21203/rs.3.rs-7553639/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7553639/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn addition to CropWat simulations, machine learning (ML) as tools of artificial intelligent (AI) algorithms were employed to enhance predictive accuracy by analyzing non-linear patterns across irrigation systems, genotypes, and planting dates. This study was conducted at the Experimental Research Farm of the National Research Centre (NRC) in El-Nubaria, Beheira Governorate, Egypt, to evaluate the impact of irrigation systems, planting dates (as a climate change proxy), and wheat genotypes on wheat performance. The field experiment involved two irrigation systems (sprinkler and drip), three Egyptian wheat varieties (Misr 1, Sakha 95, and Giza 171), and four sowing dates: the Normal Date of Planting (NDP), and 10, 20, and 30 days after NDP (DAND). The CropWat model was used to simulate biological yield, straw yield, grain yield, harvest index (HI), and water productivity (WP), with results compared to observed field data. Findings indicated that CropWat generally underestimated yields under sprinkler irrigation and overestimated them under drip, especially for Misr 1. Giza 171 showed the closest alignment between simulated and observed results, while Sakha 95 displayed high variability. Delayed planting negatively affected all yield parameters, a trend captured in simulations but with less intensity. HI values were frequently overestimated under stress conditions, and water productivity was inconsistently simulated, especially under later planting dates. Statistical analysis (LSD 0.05) confirmed that many observed-simulated differences were significant, indicating the need for improved model calibration. Conclusion include: (1) prioritizing Giza 171 for its stable performance under climate variability, (2) optimizing drip irrigation for water-use efficiency, and (3) enhancing CropWat\u0026rsquo;s climate sensitivity and varietal calibration to improve predictive accuracy under changing environmental conditions.\u003c/p\u003e","manuscriptTitle":"Modeling and Optimizing Wheat Yield under Climate Variability Using Artificial Intelligent and CropWat: A Comparative Study in Nubariya, Egypt","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 10:25:09","doi":"10.21203/rs.3.rs-7553639/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"20445ffc-ee09-4f81-8edd-4b2e99a850b0","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56493879,"name":"Earth and environmental sciences/Climate sciences"},{"id":56493880,"name":"Biological sciences/Ecology"},{"id":56493881,"name":"Earth and environmental sciences/Ecology"},{"id":56493882,"name":"Earth and environmental sciences/Environmental sciences"},{"id":56493883,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2025-10-27T15:54:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-20 10:25:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7553639","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7553639","identity":"rs-7553639","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}